{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.简单示例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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K04BJwCdm9iczOyTCtYl4VmDYG7yyd2N55e6VvZc841/Zaxr0NmSPWOWM9rEu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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x264157a7d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "x = [1, 2, 3, 1]\n",
    "y = [1, 3, 0, 2]\n",
    "plt.plot(x, y)\n",
    "plt.title('My first plot')\n",
    "plt.xlabel('x axis')\n",
    "plt.ylabel('y axis')\n",
    "# plt.xticks([1, 6])\n",
    "plt.yticks([1, 6])\n",
    "# plt.xticks([1, 6], ['a', 'b'])\n",
    "# plt.xlim([-1, 4])\n",
    "plt.ylim([-1, 3])\n",
    "plt.grid(True, color='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.简单样式"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 数据线样式设置\n",
    "\n",
    "|颜色缩写|全称|\n",
    "| ------ |:--:|\n",
    "|b|blue|\n",
    "|c|cyan|\n",
    "|g|green|\n",
    "|k|black|\n",
    "|m|magenta|\n",
    "|r|red|\n",
    "|w|white|\n",
    "|y|yellow|\n",
    "\n",
    "|线型缩写|含义|\n",
    "| ------ |:--:|\n",
    "|--|--虚线|\n",
    "|-.|-.虚线|\n",
    "|:|.虚线|\n",
    "|-|实线|\n",
    "\n",
    "|marker|参考|\n",
    "| ------ |:--:|\n",
    "|.|Point marker|\n",
    "|,|Pixel marker|\n",
    "|o|Circle marker|\n",
    "|v|Triangle down marker |\n",
    "|^|Triangle up marker |\n",
    "|<|Triangle left marker |\n",
    "|>|Triangle right marker |\n",
    "|1|Tripod down marker|\n",
    "|2|Tripod up marker|\n",
    "|3|Tripod left marker|\n",
    "|4|Tripod right marker|\n",
    "|s|Square marker|\n",
    "|p|Pentagon marker|\n",
    "|*|Star marker|\n",
    "|h|Hexagon marker|\n",
    "|H|Rotated hexagon D Diamond marker|\n",
    "|d|Thin diamond marker|\n",
    "|v|Vertical line (vlinesymbol) marker|\n",
    "|_|Horizontal line (hline symbol) marker|\n",
    "|+|Plus marker|\n",
    "|x|Cross (x) marker|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Rab2CWTWsIU0rez/3OEk20GigQgvouQSG7oWQfmDnrN8f/wB2jBdl9Yv6wLW9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\nPoNcNDKnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26416e94320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = [1, 2, 3, 1]\n",
    "y = [1, 3, 0, 2]\n",
    "\n",
    "plt.plot(x, y, color='r', linestyle='--', linewidth = 2, marker='v', label='one')\n",
    "plt.plot(np.array(x)+1, np.array(y)+1, linestyle=':', linewidth = 3, marker='d', label='two')\n",
    "plt.plot(np.array(x)+2, np.array(y)+3, linestyle='-', linewidth = 3, marker='x', label='three')\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.中文问题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OFs1VUbEUZ7v5hLLdLDEW3UMQQvQEvKSUe3XC+4EBUsrugAMwpIgxJgohwoQQ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\nhGBmZiknBDMzA5wQzMws9X/GrKstcadNRQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b2e19dd8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "mpl.rcParams['font.sans-serif'] = ['SimHei'] #指定默认字体  \n",
    "mpl.rcParams['axes.unicode_minus'] = False #解决保存图像是负号'-'显示为方块的问题  \n",
    "\n",
    "x = [1,2,3,1]\n",
    "y = [1,3,0,1]\n",
    "\n",
    "x2 = np.array(x)+1\n",
    "y2 = np.array(y)+1\n",
    "\n",
    "plt.plot(x,y,'g',label=u'第一个', linewidth=5)\n",
    "plt.plot(x2,y2,'c',label=u'第二个',linewidth=5)\n",
    "\n",
    "plt.title(u'两个三角形')\n",
    "plt.ylabel(u'Y轴')\n",
    "plt.xlabel(u'X轴')\n",
    "plt.text(1.2, 1, u'这是三角形')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 方法一：直接指定字体"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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K9abJQqlD6OjuYU9TB0WWJAvP8FkdEaVsQJOFUoews6EdCN0yH768Heo610LZQciThYgs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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2c12f7bf550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-   \n",
    "from pylab import *\n",
    "myfont = matplotlib.font_manager.FontProperties(fname='C:/Python35/Lib/site-packages/matplotlib/mpl-data/fonts/ttf/msyh.ttf')\n",
    "mpl.rcParams['axes.unicode_minus'] = False  \n",
    "t = arange(-5*pi, 5*pi, 0.01)  \n",
    "y = sin(t)/t  \n",
    "plt.plot(t, y)  \n",
    "plt.title(u'这里写的是中文',fontproperties=myfont) #指定字体  \n",
    "plt.xlabel(u'X坐标',fontproperties=myfont)  \n",
    "plt.ylabel(u'Y坐标',fontproperties=myfont)  \n",
    "plt.show()  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 方法二："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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k10Fuf3Yt04en8MiXT6BfdGSbZb9y8nBKq+u5/70dHJc1gGtn5DgYaddsO1BF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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b2e2bf70b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-   \n",
    "from pylab import *  \n",
    "mpl.rcParams['font.sans-serif'] = ['SimHei'] #指定默认字体  \n",
    "mpl.rcParams['axes.unicode_minus'] = False #解决保存图像是负号'-'显示为方块的问题  \n",
    "\n",
    "t = arange(-5*pi, 5*pi, 0.01)  \n",
    "y = sin(t)/t  \n",
    "plt.plot(t, y)  \n",
    "plt.title(u'这里写的是中文')  \n",
    "plt.xlabel(u'X坐标')  \n",
    "plt.ylabel(u'Y坐标')  \n",
    "plt.show()  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 方法三："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 1、将系统字体simhei复制到C:\\Python35\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf下\n",
    "# 2、修改C:\\Python35\\Lib\\site-packages\\matplotlib\\mpl-data目录下的文件matplotlibrc（去掉#）\n",
    "#     font.family         : simhei(更改)\n",
    "#     font.serif          : simhei,(在之前添加)\n",
    "#     axes.unicode_minus  : False(更改) #作用就是解决负号'-'显示为方块的问题"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.画图公式TeX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rAL3ynzcvaVqFR+KKdu3aRSYmJpSYmChbNnHiRGrcuDHdu3dPrTby8vIoPj6+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\n02P/DyC28uArM2hDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b2e1a9bb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure() #figsize=(10,6)\n",
    "ax= fig.add_subplot(111)\n",
    "ax.set_xlim([1, 6]);\n",
    "ax.set_ylim([1, 9]);\n",
    "ax.text(2, 8,  r\"$ \\mu \\alpha \\tau \\pi \\lambda \\omega \\tau \\\n",
    "    lambda \\iota \\beta $\",color='r',fontsize=12);\n",
    "ax.text(2, 6, r\"$ \\lim_{x \\rightarrow 0} \\frac{1}{x} $\",fontsize=20);\n",
    "ax.text(2, 4, r\"$ a \\ \\leq \\ b \\ \\leq \\ c \\ \\Rightarrow \\ a \\\n",
    "    \\leq \\ c$\",fontsize=20);\n",
    "ax.text(2, 2, r\"$ \\sum_{i=1}^{\\infty}\\ x_i^2$\",fontsize=20);\n",
    "ax.text(4, 8, r\"$ \\sin(0) = \\cos(\\frac{\\pi}{2})$\",fontsize=20);\n",
    "ax.text(4, 6, r\"$ \\sqrt[3]{x} = \\sqrt{y}$\",fontsize=20);\n",
    "ax.text(4, 4, r\"$ \\neg (a \\wedge b) \\Leftrightarrow \\neg a \\\n",
    "    \\vee \\neg b$\");\n",
    "ax.text(4, 2, r\"$ \\int_a^b f(x)dx$\",fontsize=20);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lJQwbNowpU6aQmJhodxzVSLSYvODhhx8mPDyc6dOn2x1FNSLdzLPYunXreP3118nNzdVP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\nIlpMSllEi0kpi/w/tz7zVBmx1NsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b2e2c0c0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(1, figsize=(3,3))\n",
    "ax = plt.subplot(111)\n",
    "\n",
    "ann = ax.annotate(\"Test\",\n",
    "                  xy=(0.2, 0.2), \n",
    "                  xytext=(0.8, 0.8), \n",
    "                  size=20, va=\"center\", ha=\"center\",\n",
    "                  bbox=dict(boxstyle=\"round4\", fc=\"w\"),\n",
    "                  arrowprops=dict(arrowstyle=\"-|>\",\n",
    "                                  connectionstyle=\"arc3,rad=0.2\",\n",
    "                                  fc=\"r\"), \n",
    "                  )\n",
    "ax.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.多子图结构"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vf4E//Qkefxy2bXPnm5vh3/4Nvvtd+OpXYe5cmD0bfvc7d37TJvjOd7J+v2B7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\neq9zBDnskgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b2e3249860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "t = np.arange(0, 5, 0.2)\n",
    "\n",
    "fig = plt.figure() #figsize=(10,6)\n",
    "ax1 = fig.add_subplot(321)\n",
    "ax2 = fig.add_subplot(322)\n",
    "ax3 = fig.add_subplot(312)\n",
    "ax4 = fig.add_subplot(325)\n",
    "ax5 = fig.add_subplot(326)\n",
    "\n",
    "ax1.plot(t, t, 'r--', t, t**2, 'bs', t, t**3, 'g^')\n",
    "ax1.grid(True)\n",
    "ax1.set_title('plot')\n",
    "\n",
    "ax2.semilogy(t, t, 'r--', t, t**2, 'bs', t, t**3, 'g^')\n",
    "ax2.grid(True)\n",
    "ax2.set_title('ylog')\n",
    "\n",
    "ax3.loglog(t, t, 'r--', t, t**2, 'bs', t, t**3, 'g^')\n",
    "ax3.grid(True)\n",
    "ax3.set_title('loglog')\n",
    "\n",
    "ax4.semilogy(t, t, 'r--', t, t**2, 'bs', t, t**3, 'g^')\n",
    "ax4.grid(True)\n",
    "ax4.set_title('ylog')\n",
    "\n",
    "ax5.loglog(t, t, 'r--', t, t**2, 'bs', t, t**3, 'g^')\n",
    "ax5.grid(True)\n",
    "ax5.set_title('loglog')\n",
    "\n",
    "fig.suptitle('normal vs ylog vs loglog')\n",
    "fig.subplots_adjust(hspace=0.5)\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
